The present disclosure relates generally to a building management system (BMS). More particularly, the present disclosure relates to a BMS with saturation detection and removal to facilitate the generation of linear models in system identification. System identification is the process of determining a system of equations (e.g., a system model) that allow for the prediction of future system states or system outputs.
As used herein, saturation refers to the usage of a maximum or minimum HVAC capacity to track a desired temperature set-point for buildings under heating and cooling modes. Control saturation drastically influences plant models obtained when using system identification because saturation leads to nonlinear behavior. One way to deal with saturation is nonlinear system identification, which may be able to capture model dynamics under saturation. However, obtaining such models adds a degree of complexity to the system identification optimization problem, often becoming too computationally expensive to use for on-line optimization based control.
Another way to deal with saturation is saturation detection and removal. Removing saturation allows for linear models that can capture the plant dynamics in the linear range. Obtaining linear models is desirable for on-line control because linear models reduce computational complexity compared to nonlinear models. Several conventional methods are available for saturation detection and removal, including residual detection and nonlinear system detection. However, conventional methods of saturation detection assume that nonlinearity only occurs in the inputs or outputs of the system, and therefore cannot handle nonlinearity that occurs in the system's inherent dynamics (i.e., in the states). These conventional methods are therefore not well suited for saturation detection and removal in HVAC systems.